Multiple environmental factors, but not nutrient addition, directly affect wet grassland soil microbial community structure: a mesocosm study

Abstract Nutrient addition may change soil microbial community structure, but soil microbes must simultaneously contend with other, interacting factors. We studied the effect of soil type (peat, mineral), water level (low, high), and nutrient addition (unfertilized, fertilized) on wet grassland soil microbial community structure in both vegetated and un-vegetated soils after five years of treatment application in a mesocosm, using Illumina sequencing of the bacterial V4 region of the small ribosomal sub-units. Soil type, water level, and plant presence significantly affected the soil microbial structure, both singly and interactively. Nutrient addition did not directly impact microbiome structure, but acted indirectly by increasing plant biomass. The abundance of possible plant growth promoting bacteria and heterotrophic bacteria indicates the importance of bacteria that promote plant growth. Based on our results, a drier and warmer future would result in nutrient-richer conditions and changes to microbial community structure and total microbial biomass and/or abundances, with wet grasslands likely switching from areas acting as C sinks to C sources.


Introduction
Soil microbes are an important component of the tightly connected plant-soil-microbe (PSM) system (Pugnaire et al. 2019 ). PSM systems in general and the soil microbial community in particular are influenced by both biotic and abiotic factors, which are known to gr eatl y influence the abundance, community structure, and activities of soil microbes . T hese in turn can gr eatl y impact plant growth and composition and, ther efor e, both dir ectl y and indir ectl y influence ecosystem functioning (Ehrenfeld et al. 2005, Chr o ňák ová et al. 2019. Abiotic factors, such as nutrient levels, hydrology, and soil type, are known to greatly influence soil microbe diversity and abundance (Hartman andTringe 2019 , Zhao et al. 2019 ). Soil nitrogen (N) contents positiv el y affect nitrification and denitrification r ates, while the abundance of ammonia-oxidizing bacteria is greater in N-fertilized conditions (Carey et al. 2016, Nguyen et al. 2018. Changes in site hydrology can impact the amount of oxygen available for aerobic metabolism (Wang et al. 2019 ). Flooding can lead to increased metabolism and greater abundance of the anaerobic methanogenic community (Watanabe et al. 2020 ), while having a positive relationship on polyphenol oxidase and peroxidase activities (Cao et al. 2020 ). In contr ast, extensiv e dr ought can significantl y negativ el y affect soil micr obial comm unity composition and enzymatic activities because lo w-w ater activity denatures enzymes, ther eby decr easing the solubility of nutrients leading to reduced plant growth and production (Nguyen et al. 2018 ). Bacterial taxa may be highly correlated with certain soil c har acteris-tics, including the C:N ratio (Kuramae et al. 2011 ) and pH (Hansel et al. 2008 ). Micr obial composition also can differ gr eatl y between the rhizosphere and bulk soil, indicating the influence of the large spatial heterogeneity of soils as well as the great impact of plants on the soil microbial community Smalla 2009 , Schreiter et al. 2014 ).
T here ha ve been many studies on the effects of these different factors on soil microbial community structure, but most have treated these factors in isolation, as single-factor investigations (Sierra et al. 2015 ). Ho w ever, soil microbes must contend with multiple , interacting factors , se v er al of whic h may c hange sim ultaneousl y (Sierr a et al. 2015, Reese et al. 2018. Ther efor e, m ultiv ariable studies ma y pro vide a more realistic outlook on how soil micr obial structur e and functions may be affected under c hanging conditions. Determining the impact of such external drivers is a necessary step in understanding how ecosystem functions may c hange as envir onmental conditions c hange (Smith-Ramesh and Reynolds 2017 ; Bennett et al. 2018 ;DeLong et al. 2019 ).
Wet grasslands are diverse ecosystems within agricultural landsca pes, being tr ansitional between littor al, permanentl y flooded wetlands and upland grasslands . T hey ma y be found on peat or mineral substrates , but ha ve similar hydrologic characteristics and are maintained by disturbance (Joyce et al. 2016 ). Wet gr asslands ar e open, gr aminoid-dominated habitats that pr ovide many ecosystem services, including nutrient r emov al, flood atten uation, and ground water r ec har ge, as well as being important bir d habitats (Jo yce 2014 ). In Europe, w et gr asslands ar e cr eated and maintained by human activities, usually in the form of cutting for hay (Tallowin and Jefferson 1999 ). This disturbance helps to r emov e plant biomass, ther eby r educing nutrient inputs, whic h allows for the co-existence of less competitive plant species . T he important role of cutting and other human management activities means that any change in management practices, in conjunction with the impacts of larger-scale factors such as climate c hange, can gr eatl y affect the structure and functions of these habitats (Joyce and Wade 1998, Tallowin and J efferson 1999, J oyce 2014, Joyce et al. 2016. In some areas, < 1% of the estimated original extent of wet grasslands remains due to the dual impacts of a gricultur al intensification or abandonment (cessation of cutting) (Luoto et al. 2003, Joyce 2014 ).
An earlier field study, which investigated the impact of nutrient addition on plant growth and production and soil properties in tw o w et grasslands with peat or mineral soils, noted accelerated nutrient fluxes in both wet gr asslands, but especiall y in the one with peat soil (Picek et al. 2008, Edw ar ds 2015. In ad dition, n utrient le v els inter acted with site hydrology to affect plant growth and production, which likely allo w ed for the co-existence of common plant species belonging to different plant functional types (Edw ar ds and Č ížko vá 2020 ). T herefore , several en vironmental factors , acting both singl y and inter activ el y, affect the ecological functioning of these wet gr asslands; howe v er, the importance of these different factors could not be easily disentangled in the field. Determining the importance of these various environmental factors and how they may interact is quite necessary to pr operl y mana ge these important ecological systems.
In addition, while various soil properties, such as microbial nutrient contents, wer e measur ed in the field (Picek et al. 2008 ), soil micr obial comm unity composition was not included in pr e vious studies. We ther efor e established a mesocosm in order to investigate the impact of m ultiple envir onmental driv ers on soil micr obial comm unity structur e under mor e contr olled conditions than could be ac hie v ed in the field.
The objectives of this study were to determine the effect of (1) changing environmental conditions ( i.e., context dependence), namel y differ ences in soil type, hydr ology, and nutrient le v els, and (2) the presence or absence of plants on soil microbial community structure. Based on the results from our previous field study as well as liter atur e sour ces, w e hypothesized that: (1) soil microbial composition would be most impacted by nutrient addition, with the other environmental factors, water le v el and soil type , ha ving secondary impacts; (2) both single factors and their interactions will significantly influence microbial community structure; (3) there will be a context-dependent shift in the dominance of r-and K-str ategists, with r-str ategists favor ed in nutrient-ric her, moist but not saturated conditions; and (4) since the field study sho w ed that the wet gr asslands ar e plant-dominated systems (Picek et al. 2008 ), plant gr owth pr omoting micr obes (i.e. diazotr ophs, heter otr ophic bacteria) will be the dominant groups in the different treatment combinations.

Mesocosm establishment
A mesocosm was established at the University of South Bohemia in July 2009 to help disentangle the effects of soil type, water le v el, and fertilization on PSM interactions in wet grasslands using a full factorial design (Fig. 1 ). We used plants of Carex acuta , a common plant species of wet grasslands in Central Europe. Although C. acuta is considered to be a conservative, stress-tolerator species, it is co-dominant with more competitive species such as Glyceria maxima under a range of nutrient and water level conditions (Edw ar ds and Č ížko vá 2020 ). Carex acuta plants , consisting of m ulti-stemmed r amets, wer e collected fr om the field in April 2009. The r amets wer e di vided into se parate shoots with attac hed r oots, whic h wer e planted in 0.5 L containers filled with sand and placed into a shallow (15 cm depth) basin with water to 7 cm (providing saturated, but not flooded conditions) to r ecov er fr om the r emov al and tr ansplanting pr ocesses. Follo wing this tw o-w eek acclimation period, the plants were divided by height (small: < 5 cm; medium: > 5 but < 8 cm; tall: > 8 cm), and four healthy-looking plants (one small, two medium, and one tall) were planted into pots (40 × 40 × 35 cm; L x W x D), whic h wer e r andoml y distributed to the different experimental treatment combinations (Fig. 1 ). The pots were placed into basins (180 × 110 × 45 cm; L x W x D), located in an outdoor enclosure at the University of South Bohemia, in order to mor e easil y contr ol water and nutrient le v els. Eac h pot contained either a mineral soil or peat soil. The mineral soil consisted of a cambisol taken from a nearb y meado w, while the peat soil was de v eloped by mixing the miner al soil with peat amendments. Initial le v els of basic soil par ameters ar e giv en in Supplementary Table S1. Each basin was subjected to one of tw o w ater le v el tr eatments: either satur ated conditions, in whic h the water le v el was maintained at the soil surface (HW treatment), or a drier treatment, in which the water level was k e pt 15 cm below the soil surface (low-water treatment). Water was added to the basins when needed. Two holes were drilled into the sides of each basin at the correct height, depending on the particular water le v el tr eatment r andoml y assigned to eac h basin, to pr e v ent water le v els abov e the desir ed le v els . T he nutrient treatment consisted of no added nutrients (unfertilized treatment) or fertilized (n utrient ad dition, 300 kg NPK * ha −1 * yr −1 ) using a mineral NPK fertilizer (Lovofert 15-15-15 NPK, Lovochemie, a.s.). This NPK solution was applied in two half doses (mid-May and early July) to selected pots to simulate the normal pr ocedur es of local farmers. In addition, a micronutrient solution (Ruakura micronutrient solution) was added to all pots with plants to ensure that micronutrients did not become limiting. A treatment combination (soil, water, and nutrient) was assigned r andoml y to eac h basin, with there being two replicate basins for each treatment combination, for a total of 16 basins. Each basin contained ten pots, six with plants while the other four wer e un-v egetated, but all subjected to the same treatment combination. The greater number of vegetated pots was due to the need for more plant material to carry out other plant-based studies. In addition, the number of un-vegetated pots per basin was considered to be sufficient for the purposes of this study, the determination and comparison of soil microbial community structure . T he treatments were applied ov er fiv e years, until the end of the experiment in 2013. Data fr om five years of treatments (the 2013 growing season) were used for this study.

Sample collections
Soil and above-and belowground plant parts were sampled four times during 2013 to determine seasonal effects on above-and belowground plant biomass, soil physico-chemical properties [pH, soluble organic carbon (SOC), and total soluble nitrogen (TSN)], and microbial community composition: once before the growing season (March) and three times during the growing seasonspring (May), summer (July), and autumn (September). On each sampling date, two 10 × 10 cm plots were placed into selected pots fr om whic h abov egr ound material was harv ested. Then cor es Figure 1. Schematic of the full-factorial experimental treatment design used in our study. Treatments consisted of two soil types (Soil: peat, mineral), tw o w ater le v els (lo w = 15 cm belo w soil le v el, high = at soil surface), two nutrient le v els (unfertilized, fertilized = 300 kg NPK * ha −1 * yr −1 ) and vegetated or un-vegetated conditions. Treatment combinations were then randomly applied to particular basins. See text for details. of 7 cm diameter (15 cm depth) were taken within each of these plots . T he holes were re-filled with the respective soil after coring. Abov egr ound samples were separated into live and dead components for Carex and then any other species. All separated fractions were placed into labeled paper bags, dried at 65 • C, and weighed. Belo wground samples w ere carefully cleaned, separated into live and dead components, and each of these further divided into roots and rhizomes . T he fr actions wer e also placed into labeled paper bags, dried at 65 • C for at least 48 h, and weighed. Net abov egr ound and belowground primary production (NAPP and NBPP, respectiv el y) wer e calculated fr om these biomass data using the methods of Picek et al. ( 2008 ) and Edw ar ds and Č ížková ( 2020 ).
Soil cores (4 cm diameter; 15 cm depth) were taken from two r andoml y selected v egetated and un-v egetated pots in each basin for a total of four samples fr om eac h tr eatment combination. The cor es fr om the v egetated pots wer e taken in other corners of the pots unaffected by the plant sampling. These samples were then used to determine soil physico-chemical parameters and microbial community composition.

Molecular analyses
T he P o w er Soil DN A Isolation Kit (MoBio Laboratories Inc., Carlsbad, CA, USA) was used for the isolation of genomic DNA from soil according to the manufacturer's instructions with some modifications. A mini Bead-Beater (Bio-Spec Products, Inc.) was used at a speed of 6 ms −1 for 45 s for better disruption of cell w alls. DN A w as stored in 1.5-mL Eppendorf microtubes in a freezer (-20 • C) until analysis . T he quality of the extracted DN A w as verified b y electr ophor esis (1% w/v, 8 V cm −1 , and 45 min). Total DN A w as quantified using SybrGreen fluorescence methodology (Leininger et al. 2006 ).
The qPCR conditions for fungal quantification were as follows: initial denaturation (10 min, 95 • C), follo w ed b y 40 c ycles of 1 min at 95 • C, 1 min at 56 • C, and 1 min at 72 • C, and completed by fluorescence data acquisition at 72 • C used for target quantification. Fungal standards consisted of a dilution series (ranging from 10 1 to 10 7 gene copies μL −1 ) of a known amount of plasmid with a cloned amplicon from genomic Aspergillus niger DN A b y using the SSU gene-specific primers nu-SSU-0817-5 and nu-SSU1196-3 44 (Borneman and Hartin 2000 ). R 2 values for the fungal standard curv es wer e > 0.99. The slope w as betw een 3.34 and 3.53, giving an estimated amplification efficiency from 93% to 95%, respectively.
Detection limits (i.e. lo w est standar d concentration that is significantl y differ ent fr om the non-template contr ols) wer e < 100 gene copies * μL −1 for each of the genes . Samples , standards , and non-template controls were run in triplicate. Enhancers (BSA) were added to the PCR mixture to deal with potential inhibition during PCR. Also, se v er al dilutions (10x, 100x, and 1000x) for r epr esentativ e samples were tested beforehand to see the dilution effect on Ct values.
Sequencing of the pr okaryotic comm unity was conducted using the PCR primers 515F/806R to target the V4 region of the 16S rRNA gene (Ca por aso et al. 2010(Ca por aso et al. , 2011. The PCR mixture contained 13 μL MO BIO PCR-grade water, 10 μL 5 PRIME Hot Master Mix, 0.5 μL each of the forw ar d and r e v erse primers (0.2 μM final concentration), and 1 μL of genomic DN A. Samples w ere k e pt at 94 • C for 3 min to denature the DNA, with amplification proceeding for 35 cycles at 94 • C for 45 s, 50 • C for 60 s, and 72 • C for 90 s; a final extension of 10 min at 72 • C was added to ensure complete amplification. The r e v erse primer contained a 12-base err orcorrecting Golay barcode to facilitate multiplexing. Each sample was amplified in triplicate, combined, and quantified using Invitr ogen PicoGr een ® and a plate r eader, and equal amounts of DNA fr om eac h amplicon wer e pooled into a single 1.5-mL micr ocentrifuge tube. Cleaned amplicons were quantified using PicoGreen dsDNA r ea gent in 10 mM Tris buffer (pH 8.0). A composite sample for sequencing was created by combining equimolar ratios of amplicons from the individual samples and cleaned using the Ultra Clean ® htp 96-well PCR clean-up kit (MO BIO Laboratories). Purity and concentration of the samples were estimated by spectrophotometry. Amplicons of 250 bp were sequenced pair-end (150 × 150 cycles) on the Illumina MiSeq platform (Argonne National Laboratory, IL, USA).
Bacterial raw pair-end reads (150 bp) were joined using ea-utils to obtain reads of cca 250 bp length (Aronesty 2013 ). Quality filtering of reads was applied as described pr e viousl y (Ca por aso et al. 2011 ). Reads were truncated at a phred quality threshold of 20. Reads that contained ambiguities and reads whose barcode did not match an expected barcode sequence were discarded. Reads were assigned to operational taxonomic units (zOTUs = zero radius OTU) using the Uparse pipeline (Edgar 2013 ). Taxonomy was assigned to each read by accepting the ARB Silva v.132 taxonomy string of the best megablast matching the ARB Silva v.132 sequence . T his resulted in an OTU table showing the r elativ e distribution of zOTUs in each sample.
The r elativ e abundance ( = pr oportion) of eac h zOTU in eac h sample was then multiplied by the number of known copies of the 16S rRNA gene for that sample, as determined by qPCR (Bárta et al. 2017 ). This re-calculation resulted in producing absolute abundances for each zOTU, which were then used in subsequent statistical analyses (see below).
Raw sequencing data were deposited on the ENA (European nucleotide arc hiv e) serv er under the study PRJEB54693.

r/K analyses
From the normalized zO TU table , we were able to calculate the comm unity av er a ge genome SSU copy number (ACN) in each sample for the selected archaea and bacterial families (Thompson et al. 2017 ). The ACN was calculated from the raw and normalized zOTU table (the first step in the PICRUSt pipeline; Langille et al. 2013 ). SSU gene copies range from 1 to 15 in microbial genomes. Copiotr ophic (r-str ategists) micr obes ar e assumed to hav e mor e SSU gene copies in the genome; ther efor e , a higher a v er a ge ACN shows the higher proportion of copiotrophic taxa in the microbial community.

Da ta anal yses
The effects after five years of experimental treatments on soil pH, SOC, and TSN and net above-and belowground primary production (NAPP and NBPP, r espectiv el y) wer e tested by ANOVA following natural logarithm or square root transformations when needed to ac hie v e data normality and homogeneous v ariances. The ANOVAs were run using SYST A T v 11. NAPP and NBPP were estimated based on the harvested plant biomass in the four sampling times (Edw ar ds 2015 , Edw ar ds and Č ížková 2020 ).
The number of gene copies per ng DNA (based on qPCR) was used to calculate the bacteria-to-fungi (B/F) ratio. The sequenced archaea and bacteria had to meet several criteria in order to be included in subsequent analyses. For the analysis of the prokaryotic microbiome, we selected only the most abundant phyla ( > 1% of total bacterial zOTUs), classes ( > 3% of the zOTUs for a particular selected bacterial phylum), and families ( > 1% for a selected phylum) (Supplementary Table S2). Univariate and multivariate methods were then used to determine the effect of the experimental treatments (plant presence/absence; soil type; nutrient addition; water le v el) on the separ ate datasets of the arc haea and bacterial community structures (two datasets of, first, the selected bacterial phyla plus the Proteobacteria classes and, second, the selected families). When needed, natural logarithm or square root transformations of the data were used to meet the criteria for normality and homogeneous variances.
Differences in the soil prokaryotic microbiome among the different experimental factors were tested by running principal components analyses (PCA) and redundancy analyses (RDA) separ atel y on the archaea classes and the selected bacterial phyla in PcOrd, v. 7.0 (McCune and Mefford 2018 ). Permanov as wer e run to determine the effects of the different environmental factors, as well as the two-way interactions, on microbial composition using the "adonis" pr ogr am in the VEGAN pac ka ge in R v. 4.0.0 (Oksanen et al. 2020 , R Cor e Team 2020 ) with 999 perm utations.
Generalized linear mixed models (GLMM) were used to determine the impact of the environmental factors on individual selected phyla, classes, and families of the archaea and bacterial zO TUs . T he GLMM anal yses wer e conducted in R v. 4.0.5 (R Core Team 2021 ) with the nlme pac ka ge (Pinheir o et al. 2016 ). The experimental treatments (plants, soil, water, and nutrients) were the fixed factors, while month was a random factor. All of the mixedeffect models were run following the pr ocedur e outlined in Zuur et al. ( 2009 ). The best model for each dependent variable tested was chosen based on a comparison of Akaike information criteria (AIC) scores (Akaike 1987 ).
Differ ential anal yses wer e conducted on the natur al logarithmtransformed total zOTU counts of the selected archaea and bacterial families (Chialva et al. 2020 ). Independent analyses were performed on the environmental treatment factors (soil, water level, n utrient ad dition), as well as plant presence or absence, to show the treatment conditions preferred by the selected families . T he anal yses wer e conducted in SYST A T v. 11. Repeated measures ANOVA in SYST A T v. 11 found few significant seasonal effects in the 2013 dataset with the exception of Actinobacteria and Chlorobi. The absolute number of bacterial zOTUs in those two phyla was significantly lower in March compared to the Ma y, J uly, and October samples. Statistical analyses wer e ther efor e conducted on datasets either with or without the March data. Since the results were similar for both datasets, we report the results of the full dataset, which included all seasons.
Lastly, the effects of the experimental treatments on the differences in the distribution of r-and K-strategists were tested by Chi-square ( χ 2 ) analysis in SYST A T v. 11.

Soil physico-chemical characteristics
The peat soil was more acidic and contained more C and N than the mineral soil, both initially and after five years of experimental treatments (Supplementary Table S1). In 2013, the pH of both soils was significantly greater in the high water (HW), un-vegetated treatments (significant water level * plant interaction, F 1, 80 = 3.25, P = 0.05), while SOC and TSN le v els wer e significantl y gr eater in fertilized peat soil, but only SOC had a significant soil * nutrient interaction term (F 1, 80 = 4.01, P = 0.048). TSN was significantly greater in HW, un-vegetated samples (water level * plant interaction, F 1, 80 = 24.48, P < 0.001).
SOC and TSN le v els also differ ed ov er time, with SOC le v els being significantly higher in September (L 16, 18 = 25.66, P < 0.001). Maxim um TSN le v els occurr ed in Marc h, with these being significantl y gr eater than the September le v els (L 16, 18 = 72.658, P < 0.001).

B/F ratio
Bacteria dominated the mesocosm system being at least two orders of magnitude greater than fungal abundance, with the sample B/F ratio ranging from 38 to > 300 (Fig. 2 ). There were significantl y mor e OTUs assigned to bacteria than fungi in the miner al soil, while fungal OTUs were more numerous in peat, although these were still fewer than bacteria (significant soil effect, t = 26.106, P < 0.001). Fungi were further supported in vegetated soil (significant plant effect, t = 16.870, P < 0.001), which was more a ppar ent in the peat soil under lo w-w ater conditions (significant soil * water le v el effect; t = 9.655, P = 0.002).

Ar c haeal and bacterial microbiome
The number of obtained sequences r anged fr om 7.77 * 10 8 in March to 1.18 * 10 9 in July. Of these, archaea accounted for only 1.2%-1.3% of the sequences, neither of which differed seasonally.

Impact of the treatment factors on microbiome structure and abundance
Soil type, water le v el, plant pr esence or absence, and their interactions, significantly affected total microbial abundances ( P < 0.05; GLMM analysis), while nutrient supply had little impact. Total microbial abundance was the greatest in the peat, lo w-w ater , unfertilized, un-v egetated tr eatment, but was also greater in vegetated, HW conditions (significant water * plant interaction, P < 0.001), especially in the mineral soil (Fig. 3 ).
For the arc haea, Methanobacteria, Methanomicr obia, and Thermoplasmata (all Eury ar c haeota), whic h pr eferr ed gr owing in unv egetated, satur ated (HW) miner al soil (significant soil * water * plant interaction: F 1, 66 = 17.40; P < 0.001; Supplementary Fig. S1a), wer e separ ated fr om arc haea classes associated mor e with the peat soil (MBGA and Thaumarcheota-both Crenarchaeota-and P arv arc haea) along axis 1 of the PCA ( Supplementary Fig. S2a, b). Water le v el differ ences further separ ated the miner al-pr eferring group of Eury ar chaeota along axis 1, as well as those classes associated more with peat soil (significant soil * water interaction, P = 0.001) along axis 2 of the PCA (Supplementary Fig. S2a, b) (RDA: soil r = 0.367 and 0.481 for axes 1 and 2, r espectiv el y; water r = 0.371 and 0.309 for axes 1 and 2, r espectiv el y; Fig. 4 A). Plant pr esence or absence was an important, though secondary, factor related to axis 1 (RDA: plant r = 0.326), while both nutrient addition and plant presence were the most important factors related to axis 3 (PCA explained variance = 17.20%; RDA r = 0.272 and 0.232 for nutrients and plants, r espectiv el y).
Both soil type and water le v el wer e the main factors affecting bacterial abundance at both the phylum and family levels. At the phylum le v el, bacteria divided into two groups based on their particular environmental preferences . T he relative abundances of Bacter oidetes, Chlor oflexi, and Firmicutes (Fig. 5 ) were significantl y gr eater in satur ated, un-v egetated, miner al soils ( P < 0.05), in contrast to the Acidobacteria, Actinobacteria, and alphaand beta-Pr oteobacteria gr oups ( P < 0.001). This separ ation is also noted in the r espectiv e PCA and RDA of the absolute abundances of the selected bacterial phyla. Water le v el (RDA r = 0.513; Fig. 4 B) was the main factor separating bacterial phyla along axis 1 of the PCA, with those preferring saturated conditions on the right side of axis 1 (Supplementary Fig. S3). Phyla that pr eferr ed miner al soil separ ated fr om those associated with the peat soil along the second axis (RDA r = 0.441; Fig. 4 B; Supplementary Fig. S3).
Bacterial phyla abundances changed little between months, except for Actinobacteria ( P = 0.021), Chlorobi ( P = 0.004), and delta-Proteobacteria ( P < 0.001). Actinobacteria had a significantly gr eater pr oportion of the total selected phyla during the growing season (lo w est proportions in Mar ch), while the other tw o had significantly higher proportions in March and September.
The first two axes of the selected bacterial family PCA (Supplementary Fig. S4) explained 58.44% of the variance, being almost equal in their effect. Conv ersel y to the phylum analysis, soil type was the main factor related to axis 1 (RDA r = 0.903; rucomicrobia, and the alpha-and beta-Proteobacteria families pr eferr ed peat soil (Fig. 6 A).
Water le v el was the factor most related to axis 2 (RDA r = 0.555; Fig. 4 C; Supplementary Fig. S4). Most of the families pr efer entiall y gr e w in drier conditions (lo w-w ater treatment; Fig. 6 B) with these forming a group in the upper part of the RDA gr a ph (Fig. 4 C). Exceptions to this included se v er al of the methanogenic arc haea (Methanor egulaceae, Methanospirillaceae), the two Chlorobi families, some of the Bacteroidetes families, and Syntrophaceae (delta-Proteobacteria) (Fig. 6 B).
Plant presence was an important but secondary factor associated with axis 1 of the bacterial phyla analysis (RDA r = 0.338), and was also the main effect related to axis 3 of the bacterial famil y anal ysis (PCA = 7.58%; RDA r = 0.430). Most of the selected bacterial families wer e significantl y mor e abundant in v egetated samples, with the exception of se v er al Chlor obi, Chlor oflexi and Firmicutes families as well as the methanogenic archaea (Fig. 6 C).

r/K analysis
We were able to determine the strategy for 104 of the 107 selected families, with 33 identified as r-strategists and the other 71 being K-strategists (Table 1 ; Supplementary Table S2). K-strategists significantl y pr eferr ed gr o wing in the peat soil under the lo w-w ater treatment (significant soil * water level interaction), but in the HW treatment in mineral soil ( χ 2 = 11.682; P = 0.009). There was also a significant water * plant interaction ( χ 2 = 8.698; P = 0.034), in which 42 of 52 families were more abundant in the vegetated, lo w-w ater treatment, with 11 designated as r-strategists and the remaining 31 as K-strategists .

B/F r a tio
Bacteria were the dominant microbial group in our experimental system, as noted by the B/F ratio (Fig. 2 ). Such low numbers of fungi are a common feature of many wetland habitats (Gutknecht Table 1

Treatment effects
In our study, the archaea accounted for < 2% of total O TUs , which is similar to the proportion noted by Bates et al. ( 2011 ). Likewise, Proteobacteria was the most abundant phylum in our study, follo w ed b y Acidobacteria, Actinobacteria, Bacteroidetes, and Verrucomicrobia. These tend to be common phyla in many bacterial microbiome studies (e.g. see the tables in Hawkes et al. 2007and Mendes et al. 2013, Delgado-Baquerizo et al. 2018. Soil type, water le v el, and plant pr esence significantl y influenced soil microbial structure, both singly and interactively; howe v er, n utrient ad dition had little or no impact. Ther efor e, our r esults only partially support our first hypothesis . T he lack of a significant nutrient effect differs from other studies (e.g. Marschner et al. 2003, Jangid et al. 2008, Zhang et al. 2017, Wang et al. 2018, Tahovská et al. 2020, but is similar to the results of short-term fertilization studies (Buyer andKaufman 1996 , Cr ecc hio et al. 2001 ). The lack of a significant nutrient effect may be due to us recording the effects after only five years of experimental treatments, which may be too short of a time for changes to become a ppar ent (Marschner et al. 2003 ).
In addition, application of inorganic fertilizers, as done in our study, pr omotes indir ect effects of nutrient addition by increasing plant biomass, while dir ect a pplication of or ganic matter can lead to a significant increase in bacterial biomass as well as favoring copiotr ophs ov er oligotr ophic micr obes (Hu et al. 1999, Marsc hner et al. 2003. Nutrient addition significantl y incr eased both NAPP and NBPP, which would result in a greater quantity of higherquality plant litter available for decomposition (Saggar et al. 1997 ;Cheng et al. 2020 ), as well as possibly greater root exudate inputs (Neumann et al. 2014 ). The addition of greater quantities of higher-quality plant inputs likely led to the significant increase in SOC in the vegetated samples, while the significant reduction of TSN when plants wer e pr esent was likely due to plant uptake r esulting fr om incr eased plant gr owth.
In our study, most of the micr obes pr efer entiall y gr e w in lowwater conditions when plants were present, while methanogens (arc haea-class Methanomicr obia) and other strict anaer obic bacteria, like se v er al Bacter oidetes families (GZKB119, Por phyr omonadaceae, notabl y Paludibacter and Rikenellaceae; Ueki et al. 2006 ;Nakasaki et al. 2020 ), and the ir on-r educing members of the Chlorobi families (Ignavibacteriaceae and Melioribacteraceae;Iinu et al. 2010 ;Podosk or oskaya et al. 2013, Fortney et al. 2016, wer e significantl y enhanced in the un-v egetated HW tr eatment samples . T hus , our water le v el tr eatment separ ated aer obic microbes from those able to tolerate low oxygen or anaerobic conditions, similar to other studies that noted differential soil micr obial structur e r esulting fr om c hanged hydr ologic patterns (Unger et al. 2009, Wang et al. 2015Gonzalez et al. 2016, Chialva et al. 2020. The micr obial comm unity structur e differ ed depending on the peat content of the soil, with particular phyla pr efer entiall y gr owing in either the mineral or peat soil (Lundberg et al. 2012 ). For example, most families of the Chloroflexi, Firmicutes, delta-Proteobacteria, the Eury ar chaeota, and the Nitrososphaeraceae (Cr enarc haeota) wer e associated with the mineral soil. In contrast, the Acidobacteria, Planctomycetes, Verrucomicrobia, and alphaand beta-Pr oteobacteria wer e mor e indicativ e of the peat soil, as wer e se v er al known extr emophile families, suc h as SAGMA-X (Cr enarc haeota), whic h toler ate highl y acidic conditions (Takai et al. 2001 ). Micr obial comm unity structur e v aried mor e in the peat soil, as sho wn b y the PCAs for the archaea classes and bacterial phyla and families ( Supplementary Fig. S2-4), in which the samples and centroids of microbial abundance in the mineral soil formed a tighter cluster compared to the gr eater spr ead of the peat soil samples . T his is likely related to soil pH (Hansel et al. 2008 ), the range of which was much narro w er for the mineral  Table S1b).
Similarly to Berg and Smalla ( 2009 ), we also found significant differences in microbial structure between the vegetated and unvegetated samples, but the change was only in the relative abundance of microbes at all studied levels (Schreiter et al. 2014 ). Plants can affect microbial biomass and diversity in several wa ys , but mostl y thr ough rhizode posits (Raaijmak ers et al. 2009, Neumann et al. 2014. As part of a related study, we found that C. acuta exudes mostly organic acids (unpublished data), which would incr ease soil micr obial biomass and abundance (the priming effect; J ones et al. , Kuzyako v 2010. Numerous bacteria, especially Acidobacteria, Actinobacteria, and alpha-Proteobacteria, wer e positiv el y affected by the likel y incr eased nutrient suppl y provided by the plants in our study, as also found by Mendes et al. ( 2013 ), Sc hr eiter et al. ( 2014 ), and Kielak et al. ( 2016 ).
The known ability of C. acuta to exude oxygen from its roots may be a more important method by which C. acuta affects microbial abundance and structure in its rhizosphere (Visser et al. 2000, Colmer 2003 ). Radial oxygen leakage (ROL) occurs mostly thr ough an incr ease in r oot por osity and the formation of adventi-tious roots in C. acuta (Colmer 2003 ). The leakage of oxygen would affect the soil redox state in the rhizosphere, which can greatly impact microbial community structure and functioning (Lamers et al. 2012, Tian et al. 2015, as noted by the lo w er r elativ e abundances of typical anaerobic bacterial gr oups, suc h as Chlor oflexi, Bacteriodetes , and Firmicutes , as w ell as methanogenic ar chaea, in the presence of plants.

Trea tment inter actions
The soil, water le v el, and plant factors not only had significant direct effects on the composition of the soil microbial community, but also interacted to differentially impact soil microbial abundances at all of the analyzed levels, in agreement with our second hypothesis. Soil * water, soil * plant, and water * plant were the main two-factor interactions, while significant three-way interactions (soil * water * plant) were important in affecting the relative abundance of several of the Eury ar chaeota, Actinobacteria, and delta-Proteobacteria families ( Table 1 ). The soil * plant interaction is a common two way interaction in many studies since both soil factors and plants clearly affect microbial community (A) (B) (C) Figure 6. Dot plots of significant differ entiall y abundant families between the mineral and peat soils (A); preference for either the low (15 cm below the soil surface) or high (saturated) water level treatments (B); and preference for either the vegetated or un-vegetated treatments (C). Only families that had a significant pr efer ence ( t -test; P < 0.05) are shown. Analyses were conducted on natural logarithm-transformed abundance data.
structure and functioning Smalla 2009 , Schreiter et al. 2014 ). Significant water * plant interactions occurred through the coupling of saturated water conditions, with the increased possibility of the onset of anaerobic conditions, with the ability of C. acuta to oxygenate its rhizosphere (Visser et al. 2000, Colmer 2003 ). Examples of this interaction include the methanogenic archaea ( Supplementary Fig. S5a) and iron-reducing bacteria (FRB; Supplementary Fig. S5d). Ev en though ther e was no significant nutrient effect, our r esults show that nutrient additions work through plants to affect soil microbial abundance and diversity (significant fertilization * plant inter action). This inter action was especiall y important for particular Eury ar chaeota, Acidobacteria, Actinobacteria, and alpha-, beta-, and delta-Proteobacteria families (Table 1 ). For most of these families, plant presence was a more important factor than n utrient ad dition, as e videnced by the significantl y gr eater absolute abundances when plants were either present or absent (e.g. the Eury ar chaeota), no matter the n utrient ad dition le v el. The exceptions were the Actinobacteria families C111 and Pseudonocardiaceae, in which the nutrient treatment was the differentiating factor either when plants were present (C111) or absent (Pseudonocardiaceae).
Both r-and K-str ategists wer e important soil microbial members, noting that our system was lik ely quite d ynamic (Mastný et al. 2021 ), although K-strategist families far outnumbered rstrategists . Ho w ever, only tw o factor interactions (soil * wa-ter and water * plant) significantly affected these two strategic groups, with the number of K-strategist families being significantl y gr eater in lo w-w ater peat and vegetated lo w-w ater conditions, r espectiv el y (Table 1 ). Contr ary to hypothesis 3, we also found no indication of successional change, as occurs during DOC decomposition of peat (Mastný et al. 2021 ).

Functional groups
The sequencing analyses sho w ed that the C and Fe c ycles w ere potentially the most important nutrient cycles in our experimental system at the family level, while the N cycle was of lesser importance . T his finding is in a gr eement with other studies (Laanbroek 2010 ) showing close links between the C and Fe cycles in wetlands, with ferric iron reduction being a major C sink (Neubauer et al. 2005, Sutton-Grier and Megonigal 2011, Yarwood 2018, and possibly being an important component in SOM formation (Lalonde et al. 2012 ).
In our study, microbes possibly associated with the C cycle included methanogens (Eury ar c haeota), methanotr ophs, most notably members of the gamma-Proteobacteria Methylococcaceae and the alpha-Proteobacteria Methylocystaceae families (Knief 2019 ), and those capable of organic matter degradation (Haichar et al. 2007, Sc hellenber ger et al. 2010, Kielak et al. 2016, Wieczorek et al. 2019 (Supplementary Fig. S5a-c). Like wise, micr obes in our study possibly associated with Fe(III) reduction, most notably Firmicutes and Proteobacteria families, were more abundant in HW conditions but equally preferring vegetated or un-vegetated soils ( Supplementary Fig. 5d). Iron oxidation and reduction are important cycles in wetlands (Mitsch and Gosselink 2000 ;Weiss et al. 2005, Yarwood 2018 ); ther efor e, it is not surprising that these may have a high abundance in our experimental system. Diazotrophs (Acidobacteria, Actinobacteria, Firmicutes, and v arious Pr oteobacteria families) wer e the most abundant group of microbes in our study potentially associated with the N cycle, follo w ed b y the nitrifying ar c haea (Cr enarc haeota; Supplementary  Fig. S5e, f). Both groups, along with the OM-degrading bacteria, provide nutrients to support plant gr owth, ther eby supporting hypothesis 4. The very low abundance of possible denitrifying bacteria may be connected to the gr eat ca pacity of temper ate wetland plants to take up large amounts of N (Yarw ood 2018 ); ho w e v er, this would need to be tested (Llado Fernandez et al. 2019 ).
The large variation seen in the abundances of the most common phyla shows the context-dependence of soil microbial abundance and how c hanging envir onmental conditions can significantly affect soil microbial community structure (Zhao et al. 2019 ). Interactions between the treatment factors emphasize that suc h c hanges to micr obial comm unity structur e, and thus the functions that would be expected to be supported, are not straight-forw ar d nor easily predictable . T hese findings ha ve important implications for restoring or managing wet grasslands, as well as wetlands and other ecological systems in general.

Conclusions
(1) Soil type, water le v el, and plant pr esence or absence had the largest impacts on soil micr obial comm unity structur e. Sur prisingl y, n utrient ad dition did not significantly directly impact the soil microbiome in our study.
(2) The experimental factors affected soil microbial community structure both singly and interactively. The significantl y r educed abundance of methanogens and methanotr ophs in v egetated soils indicates the ability of C. acuta to oxidize its rhizosphere. (3) The lack of a change in r-and K-str ategist pr esence and abundance (e v en under incr eased C inputs) did not support our third hypothesis that r-strategists would be favored in nutrient-richer and moist, but not saturated, conditions. (4) The abundance of possible plant gr owth-pr omoting bacteria (PGPB) and heter otr ophic bacteria indicates the importance of bacteria that promote plant gr owth, ther eby supporting hypothesis 4.
Changed precipitation patterns, as a result of climate change, are expected to affect ecosystem hydr ology, structur e, functioning, and the presence and abundance of numerous plant species (Maestra et al. 2012 ); ho w ever, w e expect that C. acuta will remain a co-dominant species in Central European wet grasslands (Edw ar ds and Č ížková 2020 ). A drier and warmer future would result in nutrient-richer conditions, and changes to microbial community structure and total microbial biomass and/or abundances, with wet grasslands likely switching from areas acting as C sinks to C sources, while the opposite would be expected under wetter, more flooded conditions (Joyce et al. 2016 ).